A complex business organization typically includes an extensive computer network system, so businesses are often continually developing and implementing advanced computer software systems in an effort to increase their level of technology. To help achieve these advances, many entities often employ computing and software professionals to lead continual efforts for software upgrades. When changing or upgrading software, computing professionals often need to determine if their software developments are sufficiently compatible within their network system. Developers usually build test environments to test new software in order to optimize the operations and facilitate its practical use. A key component in the development of new software, and common among building test environments, is the need to extract representative data which may be used as inputs during the testing process. However, it is often difficult to maintain the integrity of the extracted data, difficult to mask sensitive data, and cumbersome to enter the data. For example, data is typically manually entered, thereby causing delays and increased costs. Moreover, automating the building of test environments in a complex environment is extremely challenging and often requires a high level of knowledge, experience and technical expertise, as well as a significant level of funding.
Typically, organizations create scripts within a database management system which creates a duplicate set of database tables and populates the tables with data residing in a production database. This can be a more efficient means for creating a test database, however this step alone does not serve to create a full testing environment. Confidential data must be modified manually or a complex script must be written to conduct automatic modifications. Also, executing a suite of test jobs still requires manual execution. This can be time consuming and, as a result, may not provide the most accurate test results. Therefore, a need exists in the industry for a system and method for data extraction while retaining referential integrity.